Processing device, processing method, and medium

ABSTRACT

A processing device acquires map data that has road information, acquires detection results detected by one or more detectors that detects surroundings of a first mobile object, and cross-checks a position of a second mobile object included in the detection results with the road information of the map data to determine whether or not the position of the second mobile object is included in a region indicating a road of the road information.

CROSS-REFERENCE TO RELATED APPLICATION

Priority is claimed on Japanese Patent Application No. 2020-116374,filed Jul. 6, 2020, the content of which is incorporated herein byreference.

BACKGROUND Field

The present disclosure relates to a processing device, a processingmethod, and a medium.

Description of Related Art

In the related art, a system adapted to compare results of measuringterrestrial features with a laser scanner with terrestrial feature dataof map data and determine whether or not there are errors in the mapdata on the basis of the comparison result has been disclosed (JapaneseUnexamined Patent Application, First Publication No. 2011-27595).

SUMMARY

However, there are cases in which it is not possible to preciselyevaluate the reliability of map data in the related art.

The disclosure was made in consideration of such a circumstance, and oneof objects thereof is to more precisely evaluate the reliability of mapdata.

A processing device, a processing method, and a medium according to thedisclosure employ the following configurations.

(1) According to an aspect of the disclosure, a processing device isprovided, including: a memory that stores instructions, and one or moreprocessors that execute the instructions to: acquire map data that hasroad information, acquire detection results detected by one or moredetector that detects surroundings of a first mobile object, andcross-check a position of a second mobile object included in thedetection results with the road information of the map data to determinewhether or not the position of the second mobile object is included in aregion indicating a road of the road information.

(2) In the aforementioned aspect (1), the hardware processors determinewhether or not a chronological trajectory of the position of the secondmobile object is included in the region indicating the road and followsa shape of the road.

(3) In the aforementioned aspect (1), the second mobile object is amobile object that is moving toward the first mobile object in adirection opposite to a traveling direction of the first mobile object.

(4) In the aforementioned aspect (1), the hardware processors control aspeed and steering of the first mobile object to perform automateddriving, continue first mode automated driving in a case in which it isdetermined that the position of the second mobile object is included inthe region indicating the road of the road information when the firstmode automated driving is performed, and do not continue the first modeautomated driving in a case in which it is determined that the positionof the second mobile object is not included in the region indicating theroad of the road information when the first mode automated driving isperformed.

(5) In the aforementioned aspect (4), the hardware processors performsecond mode automated driving in a case in which it is determined thatthe position of the second mobile object is not included in the regionindicating the road of the road information when the first modeautomated driving is performed, and the second mode automated driving isautomated driving in a mode in which a rate of automation of theautomated driving is lower than a rate of automation of the first modeautomated driving or a degree of surroundings monitoring required to adriver of the first mobile object is higher than a degree ofsurroundings monitoring as compared with a case in which the first modeautomated driving is performed.

(6) In the aforementioned aspect (4), the hardware processors stop theprocess of determining whether or not the position of the second mobileobject is included in the region indicating the road of the roadinformation in a case in which it is determined that the position of thesecond mobile object is not included in the region indicating the roadof the road information when the first mode automated driving isperformed, and restart the determination at a predetermined timing afterthe determination is stopped.

(7) In the aforementioned aspect (1), the hardware processors furtherdetermine whether or not a position of a road marker line included inthe detection results conforms to a position of a road marker lineincluded in the road information.

(8) In the aforementioned aspect (7), the hardware processors control aspeed and steering of the first mobile object to perform automateddriving, and start first mode automated driving in a case in which acondition (1) of conditions (1) and (2) below is satisfied or conditions(1) and (2) are satisfied, the condition (1) being that a position of aroad marker line included in the road information conforms to a positionof a road marker line included in the detection result, and thecondition (2) being that the position of the second mobile objectincluded in the detection results is included in the region indicatingthe road of the road information.

(9) In the aforementioned aspect (1), the hardware processors control aspeed and steering of the first mobile object to perform automateddriving, and stop first mode automated driving or shift from the firstmode automated driving to second mode automated driving in a case inwhich a condition (3) below is satisfied when the first mode automateddriving is performed, the condition (3) being that the position of thesecond mobile object included in the detection results is not includedin the region indicating the road of the road information, and thesecond mode automated driving being automated driving in a mode in whicha rate of automation of the automated driving is lower than a rate ofautomation of the first mode automated driving or a degree ofsurroundings monitoring required to a driver of the first mobile objectis higher than a degree of surroundings monitoring as compared with acase in which the first mode automated driving is performed.

(10) In the aforementioned aspect (1), the hardware processors control aspeed and steering of the first mobile object on the basis of a resultof determining whether or not the position of the second mobile objectis included in the region indicating the road of the road information toperform automated driving.

(11) According to an aspect of the disclosure, a processing method isprovided, including, by a computer: acquiring map data that has roadinformation; acquiring detection results detected by one or moredetectors that detects surroundings of a first mobile object; andcross-checking a position of a second mobile object included in thedetection results with the road information of the map data anddetermining whether or not the position of the second mobile object isincluded in a region indicating a road of the road information.

(12) According to an aspect of the disclosure, a medium is provided thatstores a program that causes a computer to: acquire map data that hasroad information; acquire detection results detected by one or moredetectors that detects surroundings of a first mobile object; andcross-check a position of a second mobile object included in thedetection results with the road information of the map data to determinewhether or not the position of the second mobile object is included in aregion indicating a road of the road information.

According to (1) to (12), the processing device can more preciselyevaluate the reliability of map data by determining whether or not theposition of the second mobile object is included in the regionindicating the road of the road information. For example, the processingdevice can evaluate the reliability of map data of a road at a distantlocation since the determination is performed using the position of thesecond mobile object.

According to (2), the processing device can more precisely evaluate thereliability of map data since determination is performed using thechronological trajectory of the position of the second mobile object.

According to (3), the processing device can detect the second mobileobject at a distant location by setting a mobile object offset relativeto the first mobile object as the second mobile object. The processingdevice can thus perform the determination process at an earlier stageand evaluate the reliability of map data of a road at a furtherlocation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a configuration diagram of a control system using a processingdevice according to an embodiment.

FIG. 2 is a functional configuration diagram of a first controller and asecond controller.

FIG. 3 is a diagram explaining a determination process.

FIG. 4 is a diagram explaining the determination process.

FIG. 5 is a diagram explaining a first determination process.

FIG. 6 is a diagram (part 1) explaining a second determination process.

FIG. 7 is a diagram (part 2) explaining the second determinationprocess.

FIG. 8 is a diagram explaining a process of determining whether or notvirtual lines conform to each other.

FIG. 9 is a diagram explaining an example of first mode automateddriving and second mode automated driving.

FIG. 10 is a diagram explaining another example of the first modeautomated driving and the second mode automated driving.

FIG. 11 is a diagram explaining a relationship between a start or an endof an automated driving mode and a result of the determination process.

FIG. 12 is a flowchart showing an example of a flow of a processexecuted by an action plan generator.

FIG. 13 is a flowchart showing an example of a flow of a processexecuted by the action plan generator.

FIG. 14 is a diagram for explaining an example in which automateddriving modes are switched between.

FIG. 15 is a diagram showing an example of a functional configuration ofthe processing device.

FIG. 16 is a diagram showing an example of a hardware configuration ofan automated driving control device according to the embodiment.

DETAILED DESCRIPTION

Hereinafter, an embodiment of a processing device, a processing method,and a medium of the disclosure will be described with reference to thedrawings.

First Embodiment [Overall Configuration]

FIG. 1 is a configuration diagram of a control system 1 using aprocessing device according to an embodiment. The control system 1 ismounted in a mobile object. In the following description, the mobileobject is assumed to be a vehicle in one example. The vehicle is, forexample, a two-wheeled, three-wheeled, or four-wheeled vehicle or thelike, and a drive source thereof is an internal combustion engine suchas a diesel engine or a gasoline engine, an electric motor, or acombination thereof. The electric motor operates using power generatedby a generator coupled to the internal combustion engine or powerdischarged from a secondary battery or a fuel cell.

The control system 1 includes, for example, a camera 10, a radar device12, a light detection and ranging (LIDAR) device 14, an objectrecognition device 16, a communication device 20, a human machineinterface (HMI) 30, a vehicle sensor 40, a navigation device 50, amap-positioning unit (MPU) 60, a driving operator 80, an in-car camera90, a hands-on sensor 92, an automated driving control device 100, atraveling drive force output device 200, a brake device 210, and asteering device 220. These devices and equipment are connected to eachother via a multiplex communication line such as a controller areanetwork (CAN) communication line, a serial communication line, awireless communication network, or the like. The configuration shown inFIG. 1 is just an example, and a part of the configuration may beomitted, or other components may also be added.

The camera 10 is, for example, a digital camera using a solid imagingdevice such as a charge-coupled device (CCD) or acomplementary-metal-oxide semiconductor (CMOS). The camera 10 isattached at an arbitrary location on a vehicle with the control system 1mounted therein (hereinafter, referred to as a vehicle M). In a case inwhich a side in front is imaged, the camera 10 is attached to an upperportion of a front windshield, a rear surface of a rear-view mirror, orthe like. The camera 10 periodically and repeatedly images thesurroundings of the vehicle M, for example. The camera 10 may be astereo camera.

The radar device 12 emits electromagnetic waves such as millimeter wavesto the surroundings of the vehicle M, detects the electromagnetic waves(reflected waves) reflected by objects, and detects at least positionsof the objects (distances and azimuth directions). The radar device 12is attached to an arbitrary location of the vehicle M. The radar device12 may detect positions and speeds of objects by a frequency-modulatedcontinuous wave (FM-CW) scheme.

The LIDAR 14 emits light (or electromagnetic waves with a wavelengthclose to that of light) to the surroundings of the vehicle M andmeasures scattered light. The LIDAR 14 detects a distance to a target onthe basis of a time from light emission to light reception. The emittedlight is, for example, pulse-form laser light. The LIDAR 14 is attachedto an arbitrary location of the vehicle M.

The object recognition device 16 performs a sensor fusion process ondetection results obtained by some or all of the camera 10, the radardevice 12, and the LIDAR 14 and recognizes positions, types, speeds, andthe like of objects. The object recognition device 16 outputs therecognition result to the automated driving control device 100. Theobject recognition device 16 may output the detection results of thecamera 10, the radar device 12, and the LIDAR 14 as they are to theautomated driving control device 100. The object recognition device 16may be omitted from the control system 1.

The communication device 20 communicates with other vehicles that arepresent in the surroundings of the vehicle M using, for example, acellular network, a Wi-Fi network, Bluetooth (registered trademark),dedicated short-range communication (DSRC), or the like or communicateswith various server devices via a wireless base station.

The HMI 30 presents various kinds of information to passengers of thevehicle M and receives an input operation from the passengers. The HMI30 includes various display devices, a speaker, a buzzer, a touch panel,a switch, a key, or the like.

The vehicle sensor 40 includes a vehicle speed sensor that detects thespeed of the vehicle M, an acceleration sensor that detects anacceleration, a yaw rate sensor that detects an angular speed around avertical axis, an azimuth direction sensor that detects an orientationof the vehicle M, and the like.

The navigation device 50 includes, for example, a global navigationsatellite system (GNSS) receiver 51, a navigation HMI 52, and a routedeterminer 53. The navigation device 50 holds first map information 54in a storage device such as a hard disk drive (HDD) or a flash memory.The GNSS receiver 51 specifies the position of the vehicle M on thebasis of signals received from GNSS satellites. The position of thevehicle M may be specified or corrected with an inertial navigationsystem (INS) using outputs from the vehicle sensor 40. The navigationHMI 52 includes a display device, a speaker, a touch panel, a key, andthe like. A part or the entirety of the navigation HMI 52 may becommonly used by the aforementioned HMI 30. The route determiner 53determines a route from the position of the vehicle M specified by theGNSS receiver 51 (or an arbitrary input position) to a destination inputby a passenger using the navigation HMI 52 (hereinafter, a route on amap) with reference to the first map information 54, for example. Thefirst map information 54 is information in which road shapes areexpressed by links representing roads and nodes connected by the links,for example. The first map information 54 may include road curvatures,point-of-interest (POI) information, and the like. The route on the mapis output to the MPU 60. The navigation device 50 may perform routeguidance using the navigation HMI 52 on the basis of the route on themap. The navigation device 50 may be realized by a function of aterminal device, such as a smartphone or a tablet terminal, owned by thepassenger, for example. The navigation device 50 may transmit a currentposition and a destination to a navigation server via the communicationdevice 20 and acquire a route equivalent to the route on the map fromthe navigation server.

The MPU 60 includes, for example, a recommended lane determiner 61 andholds second map information 62 in a storage device such as an HDD or aflash memory. The recommended lane determiner 61 divides the route onthe map provided from the navigation device 50 into a plurality ofblocks (divides the route on the map at every 100 [m] in a vehicletraveling direction, for example) and determines a recommended lane foreach block with reference to the second map information 62. Therecommended lane determiner 61 determines the order of the lane from theleft the vehicle is to travel on. In a case in which there is abranching location in the route on the map, the recommended lanedeterminer 61 determines the recommended lane such that the vehicle Mcan travel through a reasonable route to travel on the route after thebranch.

The second map information 62 is map information with higher precisionthan that of the first map information 54. The second map information 62includes, for example, information regarding centers of lanes,information regarding boundaries of lanes, and the like. The second mapinformation 62 may include road information, traffic regulationinformation, address information (addresses and postal codes), facilityinformation, telephone number information, and the like. The second mapinformation 62 may be updated as needed by the communication device 20communicating with other devices.

The driving operator 80 includes, for example, an accelerator pedal, abrake pedal. a shift lever, a steering wheel, a variant steering, ajoystick, and other operators. A sensor that detects the operationamount or whether or not an operation has been performed is attached tothe driving operator 80, and the detection result is output to theautomated driving control device 100 or some or all of the travelingdrive force output device 200, the brake device 210, and the steeringdevice 220.

The in-car camera 90 images passengers who are seated on seats placedinside the vehicle (particularly, a passenger who is seated in adriver's seat), for example. The in-car camera 90 is a digital camerausing a solid imaging device such as a CCD or a CMOS. The in-car camera90 periodically images the passengers, for example.

The hands-on sensor 92 is a sensor that detects a steering wheelgripping state of the driver. The gripping state is a state in which thedriver of the vehicle M is gripping, holding, and operating the steeringwheel or a state in which the driver has put his/her hands on thesteering wheel (hand-on). The hands-on sensor 92 is an electrostaticcapacitive sensor provided to follow the circumferential direction ofthe steering wheel 82, for example. The hands-on sensor 92 detectsapproach or contact of an object (hands of the driver) with respect to aregion that is a target of detection as a change in electrostaticcapacitance. In a case in which the electrostatic capacity is equal toor greater than a threshold value, the hands-on sensor 92 outputs apredetermined detection signal to the monitor 170. In the presentembodiment, the hands-on sensor 92 detects that the driver has puthis/her hands on the steering wheel, for example.

The automated driving control device 100 includes, for example, a firstcontroller 120, a second controller 160, a monitor 170, and a storage180. Each of the first controller 120, the second controller 160, andthe monitor 170 is realized by a hardware processor such as a centralprocessing unit (CPU) executing a program (software), for example. Someor all of these components may be realized by hardware (circuit section;including a circuitry) such as a large-scale integration (LSI), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or a graphics-processing unit (GPU) or may berealized by cooperation of software and hardware. The program may bestored in a storage device (a storage device including a non-transitorystorage medium) such as an HDD or a flash memory in the automateddriving control device 100 in advance or may be installed in the HDD orthe flash memory in the automated driving control device 100 by beingstored in a detachable medium such as a DVD or a CD-ROM and by themedium (non-transitory storage medium) being attached to a drive device.The automated driving control device 100 is an example of the“processing device”.

The storage 180 is realized by an HDD, a flash memory, an electricallyerasable programmable read only-memory (EEPROM), a read-only memory(ROM), or a random-access memory (RAM), for example. The storage 180stores, for example, a program and the like that are to be executed bythe automated driving control device 100.

FIG. 2 is a functional configuration diagram of the first controller 120and the second controller 160. The first controller 120 includes, forexample, a recognizer 130 and an action plan generator 140. The firstcontroller 120 realizes a function based on artificial intelligence (AI)and a function based on a model given in advance in parallel, forexample. For example, a function of “recognizing a traffic intersection”may be realized by executing recognition of the traffic intersectionthrough deep learning or the like and recognition based on conditions(there are pattern-matchable signals, road signs, and the like) given inadvance in parallel, scoring both recognition results, andcomprehensively evaluating the scores. In this manner, the reliabilityof automated driving is secured.

The recognizer 130 includes, for example, an object recognizer 132, afirst acquirer 134A, a second acquirer 134B, a first processor 136, anda second processor 138. The recognizer 130 is an example of the“processing device”.

The object recognizer 132 recognizes types, positions, speeds,accelerations, and the like of objects that are present in thesurroundings of the vehicle M on the basis of information input from thecamera 10, the radar device 12, and the LIDAR 14 via the objectrecognition device 16. The types of the objects are types indicatingwhether the objects are vehicles, passengers, or the like, for example.The positions of the objects are recognized as positions in an absolutecoordinate system with a representative point (a center of gravity, adrive axis center, or the like) of the vehicle M as an origin(hereinafter, a vehicle coordinate system), for example, and are usedfor control. The positions of the objects may be represented byrepresentative points, such as centers of gravity or corners, of theobjects or may be represented by representative regions. The “states” ofthe objects may include accelerations or jerks of the objects or “actionstates” (for example, whether or not the vehicles are changing or tryingto change lanes).

The first acquirer 134A acquires map data that has road information. Theroad information is, for example, positions of road marker lines, typesof road sectioning lines, positions of lanes, widths of lanes, and thelike. The map data may be any map data as long as the map data includesthe road information. The second map information 62 is an example of the“map data”.

The second acquirer 134B acquires detection results detected by one ormore detectors adapted to detect surroundings of the vehicle M (firstmobile object). The camera 10, the radar device 12, and the LIDAR 14 areexamples of the “detectors”. The detectors may be the communicationdevice 20. In this case, the communication device 20 communicates with aserver device, which is not shown, and other vehicles in thesurroundings to acquire information regarding the positions of objectsin the surroundings of the vehicle M, the positions of other vehicles,and road sectioning lines.

The second acquirer 134B acquires the position of the vehicle Midentified by the vehicle sensor 40 or the navigation device 50.

The first processor 136 cross-checks the position of the vehicle Mitself specified by the navigation device 50, an image captured by thecamera 10, an output from an azimuth direction sensor included in thevehicle sensor 40, and the like with the second map information 62 andrecognizes which road and which lane in the map the vehicle M istraveling through. Further, the first processor 136 recognizes at whichposition the representative point of the vehicle M is located in thewidth direction of the lane (hereinafter, a lateral position) on thebasis of the aforementioned various kinds of information. The lateralposition may be derived as an offset amount from any one of left andright road marker lines of the lane or may be derived as an offsetamount from the center of the lane. The first processor 136 recognizesby what degree the traveling direction of the vehicle M itself at thattiming is inclined with respect to the extending direction of the lane(hereinafter, a yaw angle) on the basis of the aforementioned variouskinds of information. In a case in which the position of the vehicle Mspecified by the navigation device 50, the image captured by the camera10, the output from the azimuth direction sensor included in the vehiclesensor 40, and the like do not conform to the second map information 62to a sufficient reliability level as a result of cross-checking them,the first processor 136 outputs information indicating a cross-checkingfailure to the action plan generator 140. The “cross-checking failure”also includes a case in which there is no map corresponding to theposition of the vehicle M specified by the navigation device 50 and acase in which no road marker lines have been detected. As describedabove, the position where the vehicle M is present on the map isrecognized. Hereinafter, the process of the first processor 136cross-checking the position of the vehicle M specified by the navigationdevice 50, the image captured by the camera 10, the output from theazimuth direction sensor included in the vehicle sensor 40, and the likewith the second map information 62 will be referred to as a“cross-checking process”.

Further, the first processor 136 determines whether or not the positionof a road marker line included in the detection result (the imagecaptured by the camera 10, for example) acquired by the second acquirer134B conforms to the position of a road marker line included in the mapdata. Hereinafter, the process performed by the first processor 136 maybe referred to as a “first determination process”.

The second processor 138 cross-checks the positions of other vehiclesincluded in the detection result (the detection result of the LIDAR 14,for example) acquired by the second acquirer 134B with the roadinformation of the map data and determines whether or not the positionof a second mobile object is included in a region indicating a road ofthe road information. Hereinafter, the process performed by the secondprocessor 138 may be referred to as a “second determination process”. Ina case in which the first determination process and the seconddetermination process are not distinguished, these may be referred to asa “determination process”. Details of the determination process will bedescribed later.

The action plan generator 140 generates a target trajectory throughwhich the vehicle M is to automatically travel in the future (withoutdepending on driver's operations) such that the vehicle M travelsthrough a recommended lane determined by the recommended lane determiner61 in principle and further addresses the surrounding situation of thevehicle M. The target trajectory includes, for example, speed elements.For example, the target trajectory is expressed as points (trajectorypoints) where the vehicle M is to arrive aligned in order. Thetrajectory points are points where the vehicle M is to arrive at apredetermined traveling distance (about several [m], for example) as adistance along the road, and separately, a target speed and a targetacceleration for each predetermined sampling time (about several tenthsof [sec], for example) are generated as parts of the target trajectory.The trajectory points may be positions where the vehicle M is to arriveat a sampling clock time at every predetermined sampling time. In thiscase, information regarding the target speeds and the targetaccelerations is expressed by intervals of the trajectory points.

The action plan generator 140 may set automated driving events when thetarget trajectory is generated. The automated driving events may includea constant speed traveling event, a low speed following traveling event,a lane changing event, a branching event, a merging event, a takeoverevent, and the like. The action plan generator 140 generates the targettrajectory in accordance with activated events.

The action plan generator 140 controls the vehicle M on the basis of aresult of the determination process and a monitoring result of themonitor 170. Details of the control will be described later.

The second controller 160 controls the traveling drive force outputdevice 200, the brake device 210, and the steering device 220 such thatthe vehicle M passes through the target trajectory generated by theaction plan generator 140 at a scheduled clock time.

The second controller 160 includes, for example, an acquirer 162, aspeed controller 164, and a steering controller 166. The acquirer 162acquires information regarding the target trajectory (trajectory points)generated by the action plan generator 140 and causes a memory (notshown) to store the information. The speed controller 164 controls thetraveling drive force output device 200 or the brake device 210 on thebasis of the speed elements accompanying the target trajectory stored inthe memory. The steering controller 166 controls the steering device 220in accordance with a degree of curving of the target trajectory storedin the memory. The processes of the speed controller 164 and thesteering controller 166 are realized by a combination of feed-forwardcontrol and feed-back control, for example. In one example, the steeringcontroller 166 executes the feed-forward control in accordance with acurvature of the road in front of the vehicle M and the feed-backcontrol based on separation from the target trajectory in combination.

The monitor 170 determines whether or not the driver who is seated inthe driver's seat of the vehicle M is monitoring the surroundings of thevehicle M on the basis of the image captured by the in-car camera 90.The monitor 170 extracts a face image of the passenger who is seated inthe driver's seat from the image and acquires a sight direction from theextracted face image, for example. For example, the monitor 170 mayacquire the sight direction of the passenger from the image through deeplearning using a neural network or the like. A neural network that hasbeen caused to learn to output a sight direction when a face image isinput is constructed in advance, for example. The monitor 170 acquiresthe sight direction of the passenger by inputting the face image of thepassenger of the vehicle M to the neural network. The monitor 170determines whether or not the passenger is monitoring the surroundingsof the vehicle M itself on the basis of whether or not the sightdirection of the passenger obtained from the image is included in arange of a monitoring target determined in advance.

Returning to FIG. 1, the traveling drive force output device 200 outputsa traveling drive force (torque) for the vehicle traveling to drivewheels. The traveling drive force output device 200 includes, forexample, a combination of an internal combustion engine, an electricmotor, a transmission, and the like and an electronic control unit (ECU)that controls them. The ECU controls the aforementioned configurationsin accordance with information input from the second controller 160 orinformation input from the driving operator 80.

The brake device 210 includes, for example, a brake caliper, a cylinderthat transmits an oil pressure to the brake caliper, an electric motorthat generates the oil pressure in the cylinder, and a brake ECU. Thebrake ECU controls the electric motor in accordance with informationinput from the second controller 160 or information input from thedriving operator 80 to output a brake torque in accordance with acontrol operation to each wheel. The brake device 210 may include, as aback-up, a mechanism that transmits the oil pressure generated throughan operation of a brake pedal included in the driving operator 80 to thecylinder via a master cylinder. The brake device 210 is not limited tothe configuration described above and may be an electronic control-typehydraulic brake device that controls an actuator in accordance withinformation input from the second controller 160 and transmits the oilpressure of the master cylinder to the cylinder.

The steering device 220 includes, for example, a steering ECU and anelectric motor. The electric motor causes a force to act on arack-and-pinion mechanism to change an orientation of turning wheels,for example. The steering ECU drives the electric motor in accordancewith the information input from the second controller 160 or theinformation input from the driving operator 80 and changes anorientation of the turning wheels.

[Determination Process]

FIG. 3 is a diagram explaining the determination process. The firstprocessor 136 acquires position information (coordinates indicating theposition, for example) of a road marker line obtained from an imagecaptured by the camera 10 (S1). Next, the first processor 136 acquiresmap data included in the second map information 62 (S2). Then, the firstprocessor 136 determines whether or not the position of the road markerline obtained from the image captured by the camera 10 conforms to theposition of the road marker line included in the map data (S3) andoutputs a determination result (a result of the first determinationprocess) to the action plan generator 140 (S4).

Next, the second processor 138 acquires the map data included in thesecond map information 62 (S5). Then, the second processor 138 acquiresposition information of other vehicles obtained from a detection resultof the LIDAR 14 (S6). Next, the second processor 138 determines whetheror not the positions of other vehicles detected by the LIDAR 14 areincluded in a region indicating a road included in the map data (S7) andoutputs the determination result (a result of the second determinationprocess) to the action plan generator 140 (S8). Then, the action plangenerator 140 determines an automated driving mode on the basis of thedetermination result.

FIG. 4 is a diagram explaining the determination process. The vehicle Mis traveling through a lane L1 of a road. A different vehicle m1 istraveling in front of the vehicle M, and a different vehicle m2 istraveling behind the vehicle M. A different vehicle m3 is traveling infront of the vehicle M through a lane L2 that is an opposite lane of thelane L1. At this time, the first processor 136 performs a cross-checkingprocess and determines whether or not the cross-checking hassuccessfully been performed. In a case in which the cross-checking hassuccessfully been performed, that is, in a case in which the position ofthe vehicle M on the map has been specified, the first processor 136performs the first determination process.

FIG. 5 is a diagram explaining the first determination process. Thefirst processor 136 recognizes a road marker line in an image plane byextracting edge points with large luminance differences from adjacentpixels in the image and connecting the edge points and converts theposition of each of point of the road maker line into a vehiclecoordinate system. The first processor 136 superimposes the position ofthe vehicle M, the position of the road marker line, and the position ofthe road marker line obtained from the map data on a plane of thevehicle coordinate system and determines whether or not the position ofthe road marker line conforms to the position of the road marker lineobtained from the map data. “Conform” means that a degree of separationbetween the position of the road marker line obtained from the image andthe position of the road marker line obtained from the map data is equalto or less than a threshold value, for example. FIG. 5 shows an exampleof a case in which it is determined that the positions of the roadmarker lines conform to each other.

The description will now returned to FIG. 4. The second acquirer 134Bacquires the positions of the different vehicles m1 to m3 recognized bythe object recognizer 132. The second processor 138 extracts a differentvehicle (or a different vehicle located on the frontmost side within arecognizable range) located on the front side of the vehicle M by apredetermined distance from among the different vehicles m1 to m3. In acase in which a plurality of other vehicles are present in front of thevehicle M by the predetermined distance, the second processor 138extracts a different vehicle that is offset by a predetermined degree inthe width direction or a different vehicle that is offset in the highestlevel with respect to the center axis direction (or the travelingdirection) of the vehicle M. In the example in FIG. 4, the differentvehicle m3 is extracted as a different vehicle that is a target (secondmobile object).

The different vehicle that is a target is suitably a different vehiclethat is moving toward the vehicle M in a direction opposite to thedirection in which the vehicle M travels. The different vehicle istraveling through the opposite lane and is offset in the width directionwith respect to the vehicle M. In a case in which different vehicles arepresent in front of and behind the vehicle M as in FIG. 4, therecognizer 130 can detect different vehicles that are present in theopposite lane that are present at further positions although therecognizer 130 cannot recognize different vehicles traveling through thetraveling lane and located at distant locations. Further, differentvehicles that are offset can be more easily detected than differentvehicles that are present in front of and behind the vehicle M even in acurved road or the like. Therefore, the second processor 138 can obtainthe determination result of the second determination process at anearlier stage by setting the different vehicle m3 as a target.

The road marker line that is a target of the first determination processis a marker line at a first distance from the vehicle M, and thedifferent vehicle that is a target of the second determination processis a different vehicle at a second distance from the vehicle M. Thesecond distance is a distance that is longer than the first distance.The first distance is a distance (several meters, for example) near thevehicle M, and the second distance is a distance from a differentvehicle that is distant (several tens of meter to several hundreds ofmeter) from the vehicle M. In this manner, the automated driving controldevice 100 can check conformity between the map and the recognitionresult at the position near the vehicle M and conformity between the mapand the recognition result at the position distant from the vehicle M.

FIG. 6 is a diagram (part 1) explaining the second determinationprocess. The second processor 138 determines whether or not the positionof the different vehicle m3 is included in a region AR indicating theroad obtained from the map data in a case in which the position of thedifferent vehicle m3 detected by the LIDAR 14 is plotted in the regionAR in the vehicle coordinate system. FIG. 6 is an example in which it isdetermined that the position of the different vehicle m3 is included inthe region AR.

In a case in which the second processor 138 determines that the positionof the different vehicle m3 is included in the region AR, the differentvehicle m3 is traveling through the road corresponding to the map data,and it is thus estimated that the map data has high reliability. On theother hand, in a case in which the second processor 138 determines thatthe position of the different vehicle m3 is not included in the regionAR, the different vehicle m3 is not traveling through the roadcorresponding to the map data, and it is thus estimated that the mapdata does not have high reliability. In this case, it is estimated thatthe shape and the position of the road in the map data and the actuallydetected shape and position of the road are different from each otherbecause the map data is old or due to a temporary event (such asconstruction or traffic regulations), for example. In this manner, therecognizer 130 can more precisely evaluate the reliability of the mapdata using the position of the different vehicle and the map data.

FIG. 7 is a diagram (part 2) explaining the second determinationprocess. The second processor 138 may plot chronological positions ofthe different vehicle m3 on the map and determine whether or not theposition is included in the region indicating the road and thetrajectory of the position follows the shape of the road on the map inthe second determination process. For example, the fact that thetrajectory of the position follows the shape of the road on the mapmeans that a virtual line A conforms to a virtual line B that indicatesthe shape of the road. The virtual line A is formed by connectingpositions at a clock time t-3 to a clock time t aligned in chronologicalorder on the map with a virtual line as shown in FIG. 7 and thatindicates a history of the positions.

FIG. 8 is a diagram explaining a process of determining whether or notthe virtual lines conform to each other. The second processor 138generates a virtual line L1 connecting the positions at the clock timet-3 to the clock time t and a virtual line L2 indicating the shape ofthe road. The virtual line L2 is a line corresponding to the center ofthe road, the road marker line, or an end of the road (edge stone), forexample, or a line obtained by extending the virtual line in anextending direction of the road starting from the position where thedifferent vehicle is present (the position at the clock time t-3, forexample).

The second processor 138 determines whether or not the trajectory of theposition follows the shape of the road on the map on the basis of anangle formed by the virtual line L1 and the virtual line L2 (or thevirtual line L2 offset to be superimposed on the virtual line L1). Asshown in FIG. 8, the second processor 138 determines that the trajectoryof the position does not follow the shape of the road on the map in acase in which the formed angle θ is equal to or greater than a referenceangle θs. For example, the second processor 138 determines that thetrajectory of the position follows the shape of the road on the map in acase in which the formed angle θ is less than the reference angle θs. Inthis manner, the recognizer 130 can more precisely evaluate thereliability of the map data using the history of the position of thedifferent vehicle and the map data.

As described above, the recognizer 130 performs the first determinationprocess and the second determination process and evaluates thereliability of the map data. The evaluation result is output to thefirst controller 120, and the first controller 120 controls the vehicleM on the basis of the evaluation result.

For example, the second processor 138 may determine whether or not thedifferent vehicle is traveling through a correct lane, and in a case inwhich the different vehicle is traveling through the correct lane, thesecond processor 138 may determine that the reliability of the map datais high. For example, the second processor 138 specifies the travelingdirection of the different vehicle on the basis of the history of theposition of the different vehicle. In a case in which the differentvehicle is a different vehicle that is approaching the vehicle M, thesecond processor 138 determines whether or not the position of thedifferent vehicle is included in the opposite lane (or whether or notthe history of the position follows the shape of the opposite lane), andin a case in which positive determination is obtained, the secondprocessor 138 determines that the different vehicle is traveling throughthe correct lane. As described above, the second processor 138 canfurther improve determination precision by performing the seconddetermination process in consideration of the moving direction of thedifferent vehicle and the type of the lane.

[Automated Driving Mode]

The action plan generator 140 of the first controller 120 executes atleast first mode automated driving and second mode automated driving.The first mode automated driving is a mode in which a rate of automation(degree of automation) of automated driving is higher than that of thesecond mode automated driving. The fact that the rate of automation ofautomated driving is high means that a degree in which the firstcontroller 120 controls steering or acceleration/deceleration is high (adegree in which the driver is required to intervene the operation ofsteering or acceleration/deceleration is low). The first mode automateddriving is a mode in which duties of the driver (tasks that the driveris required to perform, required behaviors) are smaller than that in thesecond mode automated driving. The rate of automation of automateddriving links to a surroundings monitoring state or a steering grippingstate, for example.

FIG. 9 is a diagram explaining an example of the first mode automateddriving and the second mode automated driving. The first mode automateddriving is a mode that is executed under a condition that the driver ismonitoring at least the surroundings. The second mode automated drivingis a mode executed under a condition that the driver is monitoring atleast the surroundings and in a hands-on state.

FIG. 10 is a diagram explaining another example of the first modeautomated driving and the second mode automated driving. The first modeautomated driving may be a mode that does not require the driver tomonitor the surroundings and is executed even if the driver is not inthe hands-on state. The second mode automated driving may be a modeexecuted under a condition that the driver monitors at least thesurroundings.

The second mode automated driving may be a mode in which a degree of asurroundings monitoring duty that the driver of the vehicle M isrequired to have is higher as compared with a case in which the firstmode automated driving is performed. For example, the first modeautomated driving and the second mode automated driving may be modes inwhich the surroundings monitoring duty is required, and the second modeautomated driving may be a mode in which a degree of the surroundingsmonitoring duty that the driver of the vehicle M is required to have ishigher (a mode in which it is more necessary for the driver to monitorthe surroundings) as compared with the case in which the first modeautomated driving is performed. The mode in which it is more necessaryfor the driver to monitor the surroundings means that a frequency of themonitoring has to be higher or display of an image or a video that isnot related with the traveling and the driving of the vehicle M on adisplay of the vehicle M is limited.

[Utilization of Evaluation Result]

The action plan generator 140 determines whether to start the first modeautomated driving, whether to end the first mode automated driving, orwhether to start the second mode automated driving on the basis of theresult of the determination process.

FIG. 11 is a diagram explaining a relationship between a start or an endof the automated driving mode and a result of the determination process.The condition of the start of the first mode is that at least the resultof the first determination process is positive. In the condition of thestart of the first mode, the result of the second determination processmay be positive or may not be taken into consideration.

The condition of the end of the first mode is that at least the resultof the second determination process is negative. In the condition of theend of the first mode, the result of the first determination process maynot be taken into consideration.

The condition of the start of the second mode is that at least theresult of the first determination process is positive. In the conditionof the start of the second mode, the result of the first determinationprocess or the result of the second determination process may not betaken into consideration.

As described above, in a case in which the result of the seconddetermination process is negative in a state in which the first modeautomated driving is being performed, the first mode automated drivingis not continued, and the state of the automated driving moves on to thesecond mode automated driving.

[Flowchart (Part 1)]

FIG. 12 is a flowchart showing an example of a flow of a processexecuted by the action plan generator 140. First, the action plangenerator 140 determines whether or not the condition of the start ofthe first mode automated driving is satisfied (Step S100). In a case inwhich the condition of the start of the first mode automated driving issatisfied, the action plan generator 140 executes the first modeautomated driving (Step S102). In this manner, the process of oneroutine in the flowchart is ended.

[Flowchart (Part 2)]

FIG. 13 is a flowchart showing an example of a flow of a processexecuted by the action plan generator 140. For example, the flowchart isstarted after the process in the flowchart in FIG. 12 is ended. First,the action plan generator 140 determines whether or not the condition ofthe end of the first mode automated driving is satisfied (Step S200). Ina case in which the condition of the end of the first mode automateddriving is satisfied, the action plan generator 140 determines whetheror not the condition of the start of the second mode automated drivingis satisfied (Step S202).

In a case in which the condition of the start of the second modeautomated driving is satisfied, the action plan generator 140 starts thesecond mode automated driving (Step S204). In a case in which thecondition of the start of the second mode automated driving is notsatisfied, the action plan generator 140 executes a process of startingan automated driving mode (or driving assistance) in which a degree ofautomated driving is lower than that of the second mode automateddriving or of moving on to manual driving from the automated driving(Step S206). The driving assistance is assistance, representative ofwhich includes an adaptive cruise control system (ACC) for travelingwith a distance between a vehicle traveling ahead and the vehicle Mmaintained at a predetermined distance and a lane keeping assist system(LKAS) that causes the vehicle M to travel with the distance between theroad marker line of the lane through which the vehicle M travels and thevehicle M constantly maintained.

In this manner, the automated driving control device 100 controls theautomated driving mode on the basis of the result of the determinationprocess. For example, since the reliability of the map data is moreprecisely evaluated as described above, the automated driving controldevice 100 can realize control in further consideration of thereliability level of information regarding the situation of thesurroundings of the vehicle M that is held by the automated drivingcontrol device 100. In this manner, the reliability of automated drivingcontrol is improved.

For example, although it is possible to determine the reliability of themap data using the positions of terrestrial features in the surroundingsof the road, the positions of terrestrial features of the road, and themap data, the reliability is more precisely determined in a case inwhich the reliability of the map data is determined using the positionof the mobile object that is actually traveling on the road and the mapdata as compared with the stationary terrestrial features. Further, thereliability of the map data is more precisely determined using themobile object even in a case in which there are no terrestrial features(for example, a road located in a rice field) in the surroundings of theaforementioned road and the like.

Further, the recognizer 130 can determine the reliability level of themap data of a further location by employing the mobile object that is athree-dimensional object as in the present embodiment as a determinationtarget instead of employing a non-three-dimensional target such as aroad marker line as a determination target. Since the reliability levelof the map data at the further location can be determined, the automateddriving control device 100 can more quickly perform control related toautomated driving. In a case in which it is determined that thereliability level of map data on the front side by a predetermineddistance is low, for example, the automated driving control device 100can perform a process of switching the automated driving to the secondmode automated driving (a process of providing a notification to performsurroundings monitoring or hands-on to the passenger) at an earlierstage. In this manner, the passenger can prepare for the mode switchingwith enough time.

As described above, according to the present embodiment, the reliabilitylevel of the automated driving is improved by the recognizer 130 moreprecisely determining the reliability of the map data and by the actionplan generator 140 performing the control related to the automateddriving on the basis of the determined reliability level of the mapdata.

FIG. 14 is a diagram explaining an example in which automated drivingmodes are switched. For example, the map data held by the vehicle M maybe the latest for some areas or may be of an older version than thelatest version. For example, it is assumed that map data in an area A,an area B, and an area D is the latest while map data in an area C is ofan older version than the latest version. It is also assumed that themap data in the area C is different from an actual road shape in thearea C.

For example, the vehicle M executes the first mode automated driving inthe area A and the area B, and the second determination result isnegative immediately before the vehicle M enters the area C (or afterthe vehicle M enters the area C). In this case, since the condition toexecute the first mode automated driving is not satisfied any more, thevehicle M shifts the first mode automated driving to the second modeautomated driving. Next, if the results of the first determinationprocess and the second determination process become positive immediatelybefore the vehicle M enters an area D (or after the vehicle M enters thearea D), and the condition to execute the first mode automated drivingis satisfied, then the vehicle M executes the first mode automateddriving.

In this manner, the vehicle M performs automated driving with highreliability on the basis of the surrounding situation and theinformation that the vehicle M itself holds.

Although the example of the map data of an old version has beendescribed in the aforementioned example, the result of the firstdetermination process may be negative due to construction or trafficregulations even if the map data is the latest, for example. The firstmode automated driving is ended as described above in such a case aswell.

The second determination process may be stopped in a case in which theresult of the second determination process is negative in a first roadlink, and the second determination process may be restarted in a case inwhich the vehicle M has approached the next adjacent road link. In acase in which the result of the second determination process isnegative, and the second determination process is stopped, the seconddetermination process may be restarted after a predetermined timeelapses from the timing when the result of the second determinationprocess becomes negative or after the vehicle M travels by apredetermined distance. For example, in a case in which it is determinedthat the position of the second mobile object is not included in theregion indicating the road of the road information when the first modeautomated driving is preformed, the automated driving control device 100stops the process of determining whether or not the position of thesecond mobile object is included in the region indicating the road ofthe road information and restarts the determination at a “predeterminedtiming” after the determination is stopped. The “predetermined timing”is, for example, a “timing at which the vehicle M has approached thenext road link”, a “timing at which a predetermined time has elapsedfrom a timing at which the second determination process is stopped or atiming at which the vehicle M has traveled by a predetermined distance”,or a “timing at which the first mode automated driving has beenrestarted”. The automated driving control device 100 stops the firstmode automated driving in a case in which the result of the seconddetermination process is negative. The condition under which theautomated driving control device 100 restarts the first mode automateddriving is that one or both of the determination results of the firstdetermination process and the second determination process are positive,for example. Through these processes, an unnecessary process is reduced,and the second determination process is performed at an appropriatetiming. Moreover, the first mode automated driving is restarted at amore appropriate timing.

According to the embodiment described above, the recognizer 130 can moreprecisely evaluate the reliability of the map data by cross-checking theposition of the second mobile object included in the detection resultwith the road information of the map data and determining whether or notthe position of the second mobile object is included in the regionindicating the road of the road information. Moreover, the automateddriving control device 100 can perform automated driving with higherreliability using the result of evaluating the map data with higherreliability.

Although the above embodiment has been described on the assumption thatthe first acquirer 134A, the second acquirer 134B, the first processor136, and the second processor 138 are included in the automated drivingcontrol device 100, functional sections that have functions similar tothose of these functional sections may be included in a processingdevice 300 instead as shown in FIG. 15. The processing device 300 is amember separate from the automated driving control device 100. Theprocessing device 300 includes an object recognizer 302, a firstacquirer 304, a second acquirer 306, a first processor 308, and a secondprocessor 310. The object recognizer 302, the first acquirer 304, thesecond acquirer 306, the first processor 308, and the second processor310 have functions equivalent to those of the first acquirer 134A, thesecond acquirer 134B, the first processor 136, and the second processor138, respectively. In this case, the first acquirer 304 acquires mapdata from another device or the like, and the second acquirer 306acquires a detection result from another device or the like (a detectionresult of a camera, a radar device, a LIDAR, or the like). The automateddriving control device 100 acquires the determination result from theprocessing device 300 and executes control on the basis of the acquireddetermination result.

[Hardware Configuration]

FIG. 16 is a diagram showing an example of a hardware configuration ofthe automated driving control device 100 according to the embodiment. Asshown, the automated driving control device 100 is configured such thata communication controller 100-1, a CPU 100-2, a random-access memory(RAM) 100-3 used as a working memory, a read-only memory (ROM) 100-4that stores a boot program and the like, a storage device 100-5 such asa flash memory or a hard disk drive (HDD), a drive device 100-6, and thelike are connected to each other via an internal bus or a dedicatedcommunication line. The communication controller 100-1 performscommunication with components other than the automated driving controldevice 100. The storage device 100-5 stores a program 100-5 a that theCPU 100-2 is to execute. The program is developed on the RAM 100-3 by adirect memory access (DMA) controller (not shown) or the like and isexecuted by the CPU 100-2. In this manner, some or all of the firstcontroller 120, the second controller 160, and functional sectionsincluded therein are realized.

The aforementioned embodiment can be expressed as follows.

A processing device including:

a memory that stores instructions, and

one or more processors that execute the instructions to:

acquire map data that has road information,

acquire detection results detected by one or more detection sectionsadapted to detect surroundings of a first mobile object, and

cross-check a position of a second mobile object included in thedetection results with the road information of the map data to determinewhether or not the position of the second mobile object is included in aregion indicating a road of the road information.

Although a form for implementing the disclosure has been described usingthe embodiment, the disclosure is not limited to such an embodiment, andvarious modifications and replacements can be added without departingfrom the gist of the disclosure.

What is claimed is:
 1. A processing device comprising: a memory thatstores instructions, and one or more processors that execute theinstructions to: acquire map data that has road information, acquiredetection results detected by one or more detectors that detectssurroundings of a first mobile object, and cross-check a position of asecond mobile object included in the detection results with the roadinformation of the map data to determine whether or not the position ofthe second mobile object is included in a region indicating a road ofthe road information.
 2. The processing device according to claim 1,wherein the one or more processors that execute the instructions to:determine whether or not a chronological trajectory of the position ofthe second mobile object is included in the region indicating the roadand follows a shape of the road.
 3. The processing device according toclaim 1, wherein the second mobile object is a mobile object that ismoving toward the first mobile object in a direction opposite to atraveling direction of the first mobile object.
 4. The processing deviceaccording to claim 1, wherein the one or more processors that executethe instructions to: control a speed and steering of the first mobileobject to perform automated driving, continue first mode automateddriving in a case in which it is determined that the position of thesecond mobile object is included in the region indicating the road ofthe road information when the first mode automated driving is performed,and do not continue the first mode automated driving in a case in whichit is determined that the position of the second mobile object is notincluded in the region indicating the road of the road information whenthe first mode automated driving is performed.
 5. The processing deviceaccording to claim 4, wherein the one or more processors that executethe instructions to: perform second mode automated driving in a case inwhich it is determined that the position of the second mobile object isnot included in the region indicating the road of the road informationwhen the first mode automated driving is performed, and the second modeautomated driving is automated driving in a mode in which a rate ofautomation of the automated driving is lower than a rate of automationof the first mode automated driving or a degree of surroundingsmonitoring required to a driver of the first mobile object is higherthan a degree of surroundings monitoring as compared with a case inwhich the first mode automated driving is performed.
 6. The processingdevice according to claim 4, wherein the one or more processors thatexecute the instructions to: stop the process of determining whether ornot the position of the second mobile object is included in the regionindicating the road of the road information in a case in which it isdetermined that the position of the second mobile object is not includedin the region indicating the road of the road information when the firstmode automated driving is performed, and restart the determination at apredetermined timing after the determination is stopped.
 7. Theprocessing device according to claim 1, wherein the one or moreprocessors that execute the instructions to: determine whether or not aposition of a road marker line included in the detection resultsconforms to a position of a road marker line included in the roadinformation.
 8. The processing device according to claim 7, wherein theone or more processors that execute the instructions to: control a speedand steering of the first mobile object to perform automated driving,and start first mode automated driving in a case in which a condition(1) of conditions (1) and (2) below is satisfied or conditions (1) and(2) are satisfied, the condition (1) being that a position of a roadmarker line included in the road information conforms to a position of aroad marker line included in the detection result, and the condition (2)being that the position of the second mobile object included in thedetection results is included in the region indicating the road of theroad information.
 9. The processing device according to claim 1, whereinthe one or more processors that execute the instructions to: control aspeed and steering of the first mobile object to perform automateddriving, and stop first mode automated driving or shift from the firstmode automated driving to second mode automated driving in a case inwhich a condition (3) below is satisfied when the first mode automateddriving is performed, the condition (3) being that the position of thesecond mobile object included in the detection results is not includedin the region indicating the road of the road information, and thesecond mode automated driving being automated driving in a mode in whicha rate of automation of the automated driving is lower than a rate ofautomation of the first mode automated driving or a degree ofsurroundings monitoring required to a driver of the first mobile objectis higher than a degree of surroundings monitoring as compared with acase in which the first mode automated driving is performed.
 10. Theprocessing device according to claim 1, wherein the one or moreprocessors that execute the instructions to: control a speed andsteering of the first mobile object to perform automated driving basedon a result of determining whether or not the position of the secondmobile object is included in the region indicating the road of the roadinformation.
 11. A processing method, comprising, by a computer:acquiring map data that has road information; acquiring detectionresults detected by one or more detectors that detects surroundings of afirst mobile object; and cross-checking a position of a second mobileobject included in the detection results with the road information ofthe map data and determining whether or not the position of the secondmobile object is included in a region indicating a road of the roadinformation.
 12. A non-transitory computer storage medium storing aprogram causing a computer to execute: acquire map data that has roadinformation; acquire detection results detected by one or more detectorsthat detects surroundings of a first mobile object; and cross-check aposition of a second mobile object included in the detection resultswith the road information of the map data to determine whether or notthe position of the second mobile object is included in a regionindicating a road of the road information.